



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.load(A_      ,	map_location='cpu'   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				chkpt['model']

					# We have the base model one level deeper than the original XLM repository
					SCREAMING_SNAKE_CASE							:     Any						=				{}
					for k, v in state_dict.items():
										if "pred_layer" in k:
															SCREAMING_SNAKE_CASE							:     Any						=				v
										else:
															SCREAMING_SNAKE_CASE							:     str						=				v

					SCREAMING_SNAKE_CASE							:     List[Any]						=				chkpt['params']
					SCREAMING_SNAKE_CASE							:     int						=				{n: v for n, v in config.items() if not isinstance(A_      ,	(torch.FloatTensor, numpy.ndarray)   )}

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				chkpt['dico_word2id']
					SCREAMING_SNAKE_CASE							:     Any						=				{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'      ,	''   ): i for s, i in vocab.items()}

					# Save pytorch-model
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				pytorch_dump_folder_path + '/' + WEIGHTS_NAME
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pytorch_dump_folder_path + '/' + CONFIG_NAME
					SCREAMING_SNAKE_CASE							:     Any						=				pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

					print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
					torch.save(A_      ,	A_   )

					print(F'''Save configuration file to {pytorch_config_dump_path}'''   )
					with open(A_      ,	'w'      ,	encoding='utf-8'   ) as f:
										f.write(json.dumps(A_      ,	indent=2   ) + '\n'   )

					print(F'''Save vocab file to {pytorch_config_dump_path}'''   )
					with open(A_      ,	'w'      ,	encoding='utf-8'   ) as f:
										f.write(json.dumps(A_      ,	indent=2   ) + '\n'   )


if __name__ == "__main__":
			_lowerCamelCase    :		Any		      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			_lowerCamelCase    :		List[str]		      =    parser.parse_args()
			convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					int     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      None:







    """simple docstring"""






    warnings.warn(
        """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
        """ use BeitImageProcessor instead."""     ,				UpperCamelCase__     ,				)
    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

